For an input sentence, we employ a pre-trained BERT [1]} as sentence encoder to capture the \(d\) -dimensional token embedding \(\mathbf {e}_i\)  for each token:
\(\lbrace \mathbf {e}_1, \mathbf {e}_2, ..., \mathbf {e}_L\rbrace  = BERT(\lbrace \mathbf {x}_1, \mathbf {x}_2, ..., \mathbf {x}_L\rbrace ),\) 
